Statistical quality control techniques for manufacturing processes have been available for about 70 years, and have not been widely applied in American industry until very recently. Statistical process control (SPC) is a system or philosophy which pushes any manufacturing process to reduce variability down to its stable and inherent background level.
In the process industries, process control has a special meaning and is usually understood to mean the real-time automatic regulatory control and or supervisory control of the process. The need for control arises from the fact that there are inherent disturbances in any process. Control objectives in the process industries are typically directed toward maintaining regulatory control and achieving certain economics goals in the face of measured and unmeasurable disturbances within product quality Constraints. A product quality constraint is typically an inequality relationship in a Control algorithm, e.g., product B shall contain less than 1.7% of component A. When the control objective is product quality control, the constraint typically takes the form of a defined target value and tolerance about that target.
Conventional process control utilizes combinations of open-loop and closed-loop versions to achieve the control objectives for the process. Imperfect knowledge of the process and the various disturbances operating on the process means that any practical control system requires feedback of some sort to achieve a satisfactory level of control. The chief advantage of feedback is that it can compensate, at least in theory, for any and all types of disturbance. However, feedback action does not begin until an error is observed and therefor the control action lags behind the disturbance action according to the dynamics of the process.
Feedforward control will overcome the problem of response lagging the effect of a disturbance on the controlled variable if the disturbance can be measured. Proper feedforward action will prevent an error from occurring by manipulating the appropriate process variable when a disturbance is detected. However, advance knowledge of how the process will behave for each type of disturbance is required in order to have the correct direction and magnitude of adjustment. One must also identify all the possible types of disturbances in advance.
SPC on the other hand has as its goal, the minimization of variability in key product quality characteristics. SPC techniques can be used to control and adjust independent or manipulated variables in order to maintain consistency in a dependent or output variable but lack the advantage that knowledge of process dynamics and continuous time automatic control theory brings.
Incorporation of statistical thinking into real-time continuous process control then is the crux of the problem addressed by this invention. Specifically, the use of statistical techniques combined with existing control and monitoring technology to improve the overall performance of an air-separation process.